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--- |
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language: [] |
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library_name: sentence-transformers |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:557850 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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base_model: sentence-transformers/all-MiniLM-L6-v2 |
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datasets: [] |
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metrics: |
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- pearson_cosine |
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- spearman_cosine |
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- pearson_manhattan |
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- spearman_manhattan |
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- pearson_euclidean |
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- spearman_euclidean |
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- pearson_dot |
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- spearman_dot |
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- pearson_max |
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- spearman_max |
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widget: |
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- source_sentence: Mwanamume aliyepangwa vizuri anasimama kwa mguu mmoja karibu na |
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pwani safi ya bahari. |
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sentences: |
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- mtu anacheka wakati wa kufua nguo |
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- Mwanamume fulani yuko nje karibu na ufuo wa bahari. |
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- Mwanamume fulani ameketi kwenye sofa yake. |
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- source_sentence: Mwanamume mwenye ngozi nyeusi akivuta sigareti karibu na chombo |
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cha taka cha kijani. |
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sentences: |
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- Karibu na chombo cha taka mwanamume huyo alisimama na kuvuta sigareti |
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- Kitanda ni chafu. |
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- Alipokuwa kwenye dimbwi la kuogelea mvulana huyo mwenye ugonjwa wa albino alijihadhari |
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na jua kupita kiasi |
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- source_sentence: Mwanamume kijana mwenye nywele nyekundu anaketi ukutani akisoma |
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gazeti huku mwanamke na msichana mchanga wakipita. |
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sentences: |
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- Mwanamume aliyevalia shati la bluu amegonga ukuta kando ya barabara na gari la |
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bluu na gari nyekundu lenye maji nyuma. |
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- Mwanamume mchanga anatazama gazeti huku wanawake wawili wakipita karibu naye. |
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- Mwanamume huyo mchanga analala huku Mama akimwongoza binti yake kwenye bustani. |
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- source_sentence: Wasichana wako nje. |
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sentences: |
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- Wasichana wawili wakisafiri kwenye sehemu ya kusisimua. |
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- Kuna watu watatu wakiongoza gari linaloweza kugeuzwa-geuzwa wakipita watu wengine. |
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- Wasichana watatu wamesimama pamoja katika chumba, mmoja anasikiliza, mwingine |
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anaandika ukutani na wa tatu anaongea nao. |
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- source_sentence: Mwanamume aliyevalia koti la bluu la kuzuia upepo, amelala uso |
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chini kwenye benchi ya bustani, akiwa na chupa ya pombe iliyofungwa kwenye mojawapo |
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ya miguu ya benchi. |
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sentences: |
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- Mwanamume amelala uso chini kwenye benchi ya bustani. |
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- Mwanamke anaunganisha uzi katika mipira kando ya rundo la mipira |
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- Mwanamume fulani anacheza dansi kwenye klabu hiyo akifungua chupa. |
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pipeline_tag: sentence-similarity |
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model-index: |
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- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 |
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results: |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: sts test 256 |
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type: sts-test-256 |
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metrics: |
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- type: pearson_cosine |
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value: 0.6942864389866223 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.6856061049537777 |
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name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.6885375818451587 |
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name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: 0.6872214410233022 |
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name: Spearman Manhattan |
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- type: pearson_euclidean |
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value: 0.6914785578290242 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: 0.6905722127311041 |
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name: Spearman Euclidean |
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- type: pearson_dot |
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value: 0.6799233396985102 |
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name: Pearson Dot |
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- type: spearman_dot |
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value: 0.667743621858275 |
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name: Spearman Dot |
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- type: pearson_max |
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value: 0.6942864389866223 |
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name: Pearson Max |
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- type: spearman_max |
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value: 0.6905722127311041 |
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name: Spearman Max |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: sts test 128 |
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type: sts-test-128 |
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metrics: |
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- type: pearson_cosine |
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value: 0.6891584502617563 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.6814103986417178 |
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name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.6968187377070036 |
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name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: 0.6920002958564649 |
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name: Spearman Manhattan |
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- type: pearson_euclidean |
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value: 0.7000628001426884 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: 0.6960243670969477 |
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name: Spearman Euclidean |
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- type: pearson_dot |
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value: 0.6364862920838279 |
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name: Pearson Dot |
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- type: spearman_dot |
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value: 0.6189765115954626 |
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name: Spearman Dot |
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- type: pearson_max |
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value: 0.7000628001426884 |
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name: Pearson Max |
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- type: spearman_max |
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value: 0.6960243670969477 |
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name: Spearman Max |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: sts test 64 |
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type: sts-test-64 |
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metrics: |
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- type: pearson_cosine |
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value: 0.6782226699898293 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.6755345411699644 |
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name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.6962074727926596 |
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name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: 0.689094339218281 |
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name: Spearman Manhattan |
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- type: pearson_euclidean |
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value: 0.6996133052307816 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: 0.6937517032138506 |
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name: Spearman Euclidean |
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- type: pearson_dot |
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value: 0.58122590177631 |
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name: Pearson Dot |
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- type: spearman_dot |
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value: 0.5606971476688047 |
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name: Spearman Dot |
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- type: pearson_max |
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value: 0.6996133052307816 |
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name: Pearson Max |
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- type: spearman_max |
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value: 0.6937517032138506 |
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name: Spearman Max |
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--- |
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|
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# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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|
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision 8b3219a92973c328a8e22fadcfa821b5dc75636a --> |
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- **Maximum Sequence Length:** 256 tokens |
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- **Output Dimensionality:** 384 tokens |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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|
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### Full Model Architecture |
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|
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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(2): Normalize() |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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|
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("sartifyllc/swahili-all-MiniLM-L6-v2-nli-matryoshka") |
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# Run inference |
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sentences = [ |
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'Mwanamume aliyevalia koti la bluu la kuzuia upepo, amelala uso chini kwenye benchi ya bustani, akiwa na chupa ya pombe iliyofungwa kwenye mojawapo ya miguu ya benchi.', |
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'Mwanamume amelala uso chini kwenye benchi ya bustani.', |
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'Mwanamume fulani anacheza dansi kwenye klabu hiyo akifungua chupa.', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 384] |
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|
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### Semantic Similarity |
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* Dataset: `sts-test-256` |
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
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| Metric | Value | |
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|:--------------------|:-----------| |
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| pearson_cosine | 0.6943 | |
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| **spearman_cosine** | **0.6856** | |
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| pearson_manhattan | 0.6885 | |
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| spearman_manhattan | 0.6872 | |
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| pearson_euclidean | 0.6915 | |
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| spearman_euclidean | 0.6906 | |
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| pearson_dot | 0.6799 | |
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| spearman_dot | 0.6677 | |
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| pearson_max | 0.6943 | |
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| spearman_max | 0.6906 | |
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|
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#### Semantic Similarity |
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* Dataset: `sts-test-128` |
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
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| Metric | Value | |
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|:--------------------|:-----------| |
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| pearson_cosine | 0.6892 | |
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| **spearman_cosine** | **0.6814** | |
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| pearson_manhattan | 0.6968 | |
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| spearman_manhattan | 0.692 | |
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| pearson_euclidean | 0.7001 | |
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| spearman_euclidean | 0.696 | |
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| pearson_dot | 0.6365 | |
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| spearman_dot | 0.619 | |
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| pearson_max | 0.7001 | |
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| spearman_max | 0.696 | |
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|
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#### Semantic Similarity |
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* Dataset: `sts-test-64` |
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
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| Metric | Value | |
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|:--------------------|:-----------| |
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| pearson_cosine | 0.6782 | |
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| **spearman_cosine** | **0.6755** | |
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| pearson_manhattan | 0.6962 | |
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| spearman_manhattan | 0.6891 | |
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| pearson_euclidean | 0.6996 | |
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| spearman_euclidean | 0.6938 | |
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| pearson_dot | 0.5812 | |
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| spearman_dot | 0.5607 | |
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| pearson_max | 0.6996 | |
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| spearman_max | 0.6938 | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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|
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## Training Details |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `per_device_train_batch_size`: 64 |
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- `per_device_eval_batch_size`: 64 |
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- `num_train_epochs`: 1 |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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- `batch_sampler`: no_duplicates |
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|
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 64 |
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- `per_device_eval_batch_size`: 64 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 1 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: True |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
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|
|
</details> |
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|
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### Training Logs |
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| Epoch | Step | Training Loss | sts-test-128_spearman_cosine | sts-test-256_spearman_cosine | sts-test-64_spearman_cosine | |
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|:------:|:----:|:-------------:|:----------------------------:|:----------------------------:|:---------------------------:| |
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| 0.0229 | 100 | 12.9498 | - | - | - | |
|
| 0.0459 | 200 | 9.9003 | - | - | - | |
|
| 0.0688 | 300 | 8.6333 | - | - | - | |
|
| 0.0918 | 400 | 8.0124 | - | - | - | |
|
| 0.1147 | 500 | 7.2322 | - | - | - | |
|
| 0.1376 | 600 | 6.936 | - | - | - | |
|
| 0.1606 | 700 | 7.2855 | - | - | - | |
|
| 0.1835 | 800 | 6.5985 | - | - | - | |
|
| 0.2065 | 900 | 6.4369 | - | - | - | |
|
| 0.2294 | 1000 | 6.2767 | - | - | - | |
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| 0.2524 | 1100 | 6.4011 | - | - | - | |
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| 0.2753 | 1200 | 6.1288 | - | - | - | |
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| 0.2982 | 1300 | 6.1466 | - | - | - | |
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| 0.3212 | 1400 | 5.9279 | - | - | - | |
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| 0.3441 | 1500 | 5.8959 | - | - | - | |
|
| 0.3671 | 1600 | 5.5911 | - | - | - | |
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| 0.3900 | 1700 | 5.5258 | - | - | - | |
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| 0.4129 | 1800 | 5.5835 | - | - | - | |
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| 0.4359 | 1900 | 5.4701 | - | - | - | |
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| 0.4588 | 2000 | 5.3888 | - | - | - | |
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| 0.4818 | 2100 | 5.4474 | - | - | - | |
|
| 0.5047 | 2200 | 5.1465 | - | - | - | |
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| 0.5276 | 2300 | 5.28 | - | - | - | |
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| 0.5506 | 2400 | 5.4184 | - | - | - | |
|
| 0.5735 | 2500 | 5.3811 | - | - | - | |
|
| 0.5965 | 2600 | 5.2171 | - | - | - | |
|
| 0.6194 | 2700 | 5.3212 | - | - | - | |
|
| 0.6423 | 2800 | 5.2493 | - | - | - | |
|
| 0.6653 | 2900 | 5.459 | - | - | - | |
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| 0.6882 | 3000 | 5.068 | - | - | - | |
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| 0.7112 | 3100 | 5.1415 | - | - | - | |
|
| 0.7341 | 3200 | 5.0764 | - | - | - | |
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| 0.7571 | 3300 | 6.1606 | - | - | - | |
|
| 0.7800 | 3400 | 6.1028 | - | - | - | |
|
| 0.8029 | 3500 | 5.7441 | - | - | - | |
|
| 0.8259 | 3600 | 5.7148 | - | - | - | |
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| 0.8488 | 3700 | 5.4799 | - | - | - | |
|
| 0.8718 | 3800 | 5.4396 | - | - | - | |
|
| 0.8947 | 3900 | 5.3519 | - | - | - | |
|
| 0.9176 | 4000 | 5.2394 | - | - | - | |
|
| 0.9406 | 4100 | 5.2311 | - | - | - | |
|
| 0.9635 | 4200 | 5.3486 | - | - | - | |
|
| 0.9865 | 4300 | 5.215 | - | - | - | |
|
| 1.0 | 4359 | - | 0.6814 | 0.6856 | 0.6755 | |
|
|
|
|
|
### Framework Versions |
|
- Python: 3.11.9 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.40.1 |
|
- PyTorch: 2.3.0+cu121 |
|
- Accelerate: 0.29.3 |
|
- Datasets: 2.19.0 |
|
- Tokenizers: 0.19.1 |
|
|
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## Citation |
|
|
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### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### MatryoshkaLoss |
|
```bibtex |
|
@misc{kusupati2024matryoshka, |
|
title={Matryoshka Representation Learning}, |
|
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
|
year={2024}, |
|
eprint={2205.13147}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.LG} |
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} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
year={2017}, |
|
eprint={1705.00652}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
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} |
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``` |
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